一种改进的车牌字符识别训练方案

Tianding Chen
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引用次数: 1

摘要

它利用反向传播神经网络(BPNN)作为识别系统工具。通过反向传播神经网络(BPNN)进行辨识。此外,我们改进了BPNN的一些局限性,如训练过程中学习速度慢,导致难以收敛的部分最小值,以及每当添加或删除新的训练样本时需要重新训练大量数据。对车牌识别的技术和相关模型进行了详细的描述,并给出了模型的有效性。实验结果表明,该系统可以有效地识别绝大多数车牌字符,包括10个数字和26个字母字符。识别率为90%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Notice of RetractionA scheme of improved training in license plate character recognition
It utilizes back-propagation neural network (BPNN) as the recognition system tool. The identification is done by the back propagation neural network (BPNN). Moreover, we improve BPNN some limitation, such as slow learning speed in the training process, leading to partial minimum values that are difficult to converge, and the need to retrain an enormous volume of data whenever new training samples are added or deleted. The technologies and related models used for recognizing the license plates are clearly described and given to demonstrate the effectiveness of the proposed model. Experimental results show that our system can effectively recognize most license plates character, including 10 numbers and 26 alphabet characters. The recognition rate is 90%.
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